engineering-team/self-improving-agent/skills/status/SKILL.md
Memory health dashboard showing line counts, topic files, capacity, stale entries, and recommendations.
npx skillsauth add alirezarezvani/claude-skills statusInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Quick overview of your project's memory state across all memory systems.
/si:status # Full dashboard
/si:status --brief # One-line summary
# Auto-memory directory
MEMORY_DIR="$HOME/.claude/projects/$(pwd | sed 's|/|%2F|g; s|%2F|/|; s|^/||')/memory"
# Count lines in MEMORY.md
wc -l "$MEMORY_DIR/MEMORY.md" 2>/dev/null || echo "0"
# List topic files
ls "$MEMORY_DIR/"*.md 2>/dev/null | grep -v MEMORY.md
# CLAUDE.md
wc -l ./CLAUDE.md 2>/dev/null || echo "0"
wc -l ~/.claude/CLAUDE.md 2>/dev/null || echo "0"
# Rules directory
ls .claude/rules/*.md 2>/dev/null | wc -l
| Metric | Healthy | Warning | Critical | |--------|---------|---------|----------| | MEMORY.md lines | < 120 | 120-180 | > 180 | | CLAUDE.md lines | < 150 | 150-200 | > 200 | | Topic files | 0-3 | 4-6 | > 6 | | Stale entries | 0 | 1-3 | > 3 |
For each MEMORY.md entry that references a file path:
# Verify referenced files still exist
grep -oE '[a-zA-Z0-9_/.-]+\.(ts|js|py|md|json|yaml|yml)' "$MEMORY_DIR/MEMORY.md" | while read f; do
[ ! -f "$f" ] && echo "STALE: $f"
done
📊 Memory Status
Auto-Memory (MEMORY.md):
Lines: {{n}}/200 ({{bar}}) {{emoji}}
Topic files: {{count}} ({{names}})
Last updated: {{date}}
Project Rules:
CLAUDE.md: {{n}} lines
Rules: {{count}} files in .claude/rules/
User global: {{n}} lines (~/.claude/CLAUDE.md)
Health:
Capacity: {{healthy/warning/critical}}
Stale refs: {{count}} (files no longer exist)
Duplicates: {{count}} (entries repeated across files)
{{if recommendations}}
💡 Recommendations:
- {{recommendation}}
{{endif}}
/si:status --brief
Output: 📊 Memory: {{n}}/200 lines | {{count}} rules | {{status_emoji}} {{status_word}}
/si:review to promote or clean up./si:review now./si:status --brief as a quick check anytime/si:review to identify promotion candidatestools
Code review automation for TypeScript, JavaScript, Python, Go, Swift, Kotlin, C#, .NET, Java, C, C++, Rust, Ruby, PHP, and Dart/Flutter. Analyzes PRs for complexity and risk, checks code quality for SOLID violations and code smells, generates review reports. Use when reviewing pull requests, analyzing code quality, identifying issues, generating review checklists.
tools
Use when planning, funding, scoping, or synthesizing enterprise research across workstreams — clinical study design, R&D program finance, market sizing/surveys, or product/user research. Triggers on "design this clinical study", "what sample size", "R&D budget", "burn rate", "capitalize or expense", "TAM SAM SOM", "market sizing", "survey design", "segment the market", "plan user interviews", "usability test", "synthesize research insights". Forks context to route to one of four Research-Operations sub-skills (clinical-research, research-finance, market-research, product-research) and returns a digest. Distinct from ra-qm-team (regulatory submission), finance (corporate close/valuation), research/grants (funding discovery), product-team (persona/journey/live experiments), and marketing-skill (campaign analytics).
development
Use when managing the money for an internal R&D program or portfolio — building a multi-period program budget with the F&A (indirect) split, tracking burn rate and runway against value-inflection milestones, or routing R&D cost items to a capitalize-vs-expense determination. Every budget output surfaces its assumptions block; capitalize-vs-expense is decision-support only and routes to a named finance owner — it never books an entry or decides accounting treatment. Distinct from finance/financial-analysis (corporate DCF, close, valuation) and research/grants (funding discovery — this manages money already won).
development
Use when planning and synthesizing product/user research as a method-and-repository discipline — selecting the right method for the goal (generative interviews vs usability test vs concept test vs validation), computing method-based saturation/sample size with an explicit confidence level, or synthesizing coded observations into insights while flagging single-source anecdotes. Never fabricates user insight; an insight requires recurrence across independent participants. Distinct from product-team/ux-researcher-designer (persona/journey artifacts), product-discovery (discovery-sprint planning), and experiment-designer (live A/B) — this is the research-ops method + insight-repository layer.